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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    '''数据生成及预处理'''\n",
    "    # 使用随机数模拟制造数据集\n",
    "    N = 1000    # 数据集中样本数量\n",
    "    # 每个样本包括4项，其中前面3项是各考核项目得分，第4项是相应的学生的总成绩\n",
    "    feature_names = [ 'HP', 'RT', 'RC', 'G']\n",
    "    feature_num = len(feature_names)\n",
    "\n",
    "    # 为了保持程序每次运行结果的一致性，此处设置固定的随机数种子\n",
    "    np.random.seed(0)\n",
    "    # 假设每个项目成绩的服从均值为85，标准差为10的正态分布\n",
    "    data = 10*np.random.randn(N, feature_num)+85\n",
    "    data[data<0] = 0\n",
    "    data[data>100] = 100\n",
    "    \n",
    "    # 计算总成绩\n",
    "    data[:, -1] =  0.40 * data[:, 0] + 0.12 * data[:, 1] + 0.48 * data[:, 2]\n",
    "\n",
    "    # 将原数据集拆分成训练集和测试集\n",
    "    # 这里使用80%的数据做训练，20%的数据做测试\n",
    "    # 测试集和训练集必须是没有交集的\n",
    "    ratio = 0.8\n",
    "    offset = int(data.shape[0] * ratio)\n",
    "    training_data = data[:offset]\n",
    "    test_data = data[offset:]\n",
    "\n",
    "    # 绘图显示制造的数据集中的总成绩分布\n",
    "    fig = plt.figure(figsize=(10,4))\n",
    "    ax1 = fig.add_subplot(121)\n",
    "    plt.hist(training_data[:, -1], bins=10, density=True, rwidth=0.9)\n",
    "    plt.xlabel('Scores')\n",
    "    plt.ylabel('Proportion')\n",
    "    ax1.set_title('Grades distribution of training dataset')\n",
    "    ax2 = fig.add_subplot(122)\n",
    "    plt.hist(test_data[:, -1], bins=10, density=True, rwidth=0.9)\n",
    "    plt.xlabel('Scores')\n",
    "    plt.ylabel('Proportion')\n",
    "    ax2.set_title('Grades distribution of test dataset')\n",
    "    plt.show()\n",
    "\n",
    "    # 计算训练集的最大值，最小值\n",
    "    maximums, minimums = training_data.max(axis=0), training_data.min(axis=0)\n",
    "    \n",
    "    # 对数据进行归一化处理\n",
    "    for i in range(feature_num-1):\n",
    "        data[:, i] = (data[:, i] - minimums[i]) / (maximums[i] - minimums[i])\n",
    "        \n",
    "    data[:, -1] =  0.40 * data[:, 0] + 0.12 * data[:, 1] + 0.48 * data[:, 2]\n",
    "\n",
    "    # 训练集和测试集的划分比例\n",
    "    training_data = data[:offset]\n",
    "    test_data = data[offset:]\n",
    "    return training_data, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义网络模型\n",
    "class GPNet(object):\n",
    "    '''\n",
    "    Grade Predictor Network\n",
    "    '''\n",
    "    def __init__(self, num_of_weights):\n",
    "        '''初始化网络参数'''\n",
    "        # 为了保持程序每次运行结果的一致性，此处设置固定的随机数种子\n",
    "        np.random.seed(0)\n",
    "        # 随机产生w的初始值\n",
    "        self.w = np.random.randn(num_of_weights, 1)\n",
    "        self.b = 0.\n",
    "        \n",
    "    def forward(self, x):\n",
    "        '''前向计算'''\n",
    "        z = np.dot(x, self.w) + self.b\n",
    "        return z\n",
    "    \n",
    "    def loss(self, z, y):\n",
    "        '''损失函数'''\n",
    "        error = z - y\n",
    "        num_samples = error.shape[0]\n",
    "        cost = error * error\n",
    "        cost = np.sum(cost) / num_samples\n",
    "        return cost\n",
    "    \n",
    "    def gradient(self, x, y):\n",
    "        '''计算梯度'''\n",
    "        z = self.forward(x)\n",
    "        gradient_w = (z-y)*x\n",
    "        gradient_w = np.mean(gradient_w, axis=0)\n",
    "        gradient_w = gradient_w[:, np.newaxis]\n",
    "        gradient_b = (z - y)\n",
    "        gradient_b = np.mean(gradient_b)        \n",
    "        return gradient_w, gradient_b\n",
    "    \n",
    "    def update(self, gradient_w, gradient_b, eta = 0.01):\n",
    "        '''更新参数'''\n",
    "        self.w = self.w - eta * gradient_w\n",
    "        self.b = self.b - eta * gradient_b\n",
    "        \n",
    "    def train(self, x, y, iterations=100, eta=0.01):\n",
    "        '''训练网络'''\n",
    "        losses = []\n",
    "        for i in range(iterations):\n",
    "            z = self.forward(x)\n",
    "            L = self.loss(z, y)\n",
    "            gradient_w, gradient_b = self.gradient(x, y)\n",
    "            self.update(gradient_w, gradient_b, eta)\n",
    "            losses.append(L)\n",
    "            if (i+1) % 1000 == 0:\n",
    "                print('iter {}, loss {}'.format(i, L))\n",
    "        return losses\n",
    "    \n",
    "    def eval(self, x, y, precision=0.01):\n",
    "        '''评估网络'''\n",
    "        z = self.forward(x)\n",
    "        error = np.abs(z - y)\n",
    "        accuracy = np.sum(error < precision) / error.shape[0]\n",
    "        return accuracy\n",
    "\n",
    "    def predict(self, x):\n",
    "        '''前向预测'''\n",
    "        return self.forward(x)\n",
    "    \n",
    "    def parameters(self):\n",
    "        '''获取模型参数'''\n",
    "        return self.w, self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "iter 999, loss 0.021437139924305963\n",
      "iter 1999, loss 0.010767449556008564\n",
      "iter 2999, loss 0.005771229615156358\n",
      "iter 3999, loss 0.003296947148436864\n",
      "iter 4999, loss 0.0019905510404724523\n",
      "iter 5999, loss 0.001254665162661185\n",
      "iter 6999, loss 0.0008155208715905444\n",
      "iter 7999, loss 0.0005411330313174939\n",
      "iter 8999, loss 0.0003638633127032623\n",
      "iter 9999, loss 0.0002467067746271197\n",
      "iter 10999, loss 0.00016812959734879048\n",
      "iter 11999, loss 0.000114936829638552\n",
      "iter 12999, loss 7.872132658467809e-05\n",
      "iter 13999, loss 5.3978272439877e-05\n",
      "iter 14999, loss 3.7037575140255176e-05\n",
      "iter 15999, loss 2.542404989123586e-05\n",
      "iter 16999, loss 1.7456385853556138e-05\n",
      "iter 17999, loss 1.1987497930598658e-05\n",
      "iter 18999, loss 8.232687772149945e-06\n",
      "iter 19999, loss 5.654291168348321e-06\n",
      "iter 20999, loss 3.883549243859483e-06\n",
      "iter 21999, loss 2.667399038094148e-06\n",
      "iter 22999, loss 1.8321132597084983e-06\n",
      "iter 23999, loss 1.2584030598866697e-06\n",
      "iter 24999, loss 8.643489433107739e-07\n",
      "iter 25999, loss 5.936898096817008e-07\n",
      "iter 26999, loss 4.0778456516424155e-07\n",
      "iter 27999, loss 2.8009309293750026e-07\n",
      "iter 28999, loss 1.92386359301455e-07\n",
      "iter 29999, loss 1.3214365445146902e-07\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 训练网络模型\n",
    "# 获取数据\n",
    "train_data, test_data = load_data()\n",
    "x = train_data[:, :-1]\n",
    "y = train_data[:, -1:]\n",
    "# 创建网络\n",
    "net = GPNet(3)\n",
    "num_iterations=30000\n",
    "# 启动训练\n",
    "losses = net.train(x,y, iterations=num_iterations, eta=0.01)\n",
    "\n",
    "# 画出损失函数的变化趋势\n",
    "plot_x = np.arange(num_iterations)\n",
    "plot_y = np.array(losses)\n",
    "plt.plot(plot_x, plot_y)\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('cost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "----训练得到的网络模型参数----\nw: \n [[0.40081911]\n [0.12107933]\n [0.48110295]]\nb:  -0.0019954273958142053\n"
     ]
    }
   ],
   "source": [
    "w, b = net.parameters()\n",
    "print('----训练得到的网络模型参数----')\n",
    "print('w: \\n', w)\n",
    "print('b: ', b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "测试网络模型准确度accuracy： 0.995\n"
     ]
    }
   ],
   "source": [
    "# 评估网络准确度\n",
    "accuracy = net.eval(test_data[:, :-1], test_data[:, -1:], 0.001)\n",
    "print('测试网络模型准确度accuracy：', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "同学平时得分85，大报告老师评分92，学生评分94：\n实际应得分： [90.16]\n预测得分： [[90.43]]\n"
     ]
    }
   ],
   "source": [
    "# 某同学平时得分85，大报告老师评分92，学生评分94\n",
    "x = np.array([[85, 92, 94]])\n",
    "grade = np.dot(x, np.array([0.40, 0.12, 0.48]))\n",
    "predict_grade = net.predict(x)\n",
    "print('同学平时得分85，大报告老师评分92，学生评分94：')\n",
    "print('实际应得分：', np.around(grade, 2))\n",
    "print('预测得分：', np.around(predict_grade, 2))"
   ]
  }
 ]
}